use anyhow::Result;
use serde::{Deserialize, Serialize};
use tracing::info;

/// LLM provider for AI functionality
#[derive(Debug)]
pub struct LlmProvider {
    api_key: Option<String>,
    model: String,
    base_url: String,
}

/// LLM response structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LlmResponse {
    pub content: String,
    pub model: String,
    pub usage: Option<Usage>,
    pub finish_reason: Option<String>,
}

/// Token usage information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Usage {
    pub prompt_tokens: u32,
    pub completion_tokens: u32,
    pub total_tokens: u32,
}

impl LlmProvider {
    /// Create a new LLM provider
    pub async fn new() -> Result<Self> {
        info!("Initializing LLM provider");

        // TODO: Load from configuration
        let api_key = std::env::var("CDB_AI_API_KEY").ok();
        let model = "gpt-4".to_string();
        let base_url = "https://api.openai.com/v1".to_string();

        Ok(Self {
            api_key,
            model,
            base_url,
        })
    }

    /// Create a new LLM provider synchronously
    pub fn new_sync() -> Self {
        info!("Initializing LLM provider (sync)");

        // TODO: Load from configuration
        let api_key = std::env::var("CDB_AI_API_KEY").ok();
        let model = "gpt-4".to_string();
        let base_url = "https://api.openai.com/v1".to_string();

        Self {
            api_key,
            model,
            base_url,
        }
    }
    
    /// Send a chat message to the LLM
    pub async fn chat(&self, message: &str, _context: Option<&str>) -> Result<LlmResponse> {
        info!("Sending chat message to LLM");
        
        // TODO: Implement actual LLM API call
        // This is a placeholder implementation
        
        let response = LlmResponse {
            content: format!("AI response to: {}", message),
            model: self.model.clone(),
            usage: Some(Usage {
                prompt_tokens: 10,
                completion_tokens: 20,
                total_tokens: 30,
            }),
            finish_reason: Some("stop".to_string()),
        };
        
        Ok(response)
    }
    
    /// Explain a SQL query
    pub async fn explain_sql(&self, sql: &str) -> Result<String> {
        info!("Explaining SQL query");
        
        let prompt = format!("Explain this SQL query in simple terms: {}", sql);
        let response = self.chat(&prompt, None).await?;
        
        Ok(response.content)
    }
    
    /// Generate SQL from natural language
    pub async fn generate_sql(&self, prompt: &str, schema_context: Option<&str>) -> Result<String> {
        info!("Generating SQL from natural language");
        
        let full_prompt = if let Some(schema) = schema_context {
            format!("Given this database schema:\n{}\n\nGenerate SQL for: {}", schema, prompt)
        } else {
            format!("Generate SQL for: {}", prompt)
        };
        
        let response = self.chat(&full_prompt, None).await?;
        
        // TODO: Extract SQL from response
        Ok(response.content)
    }
    
    /// Check if the provider is configured
    pub fn is_configured(&self) -> bool {
        self.api_key.is_some()
    }
    
    /// Get the current model
    pub fn model(&self) -> &str {
        &self.model
    }
    
    /// Set the model
    pub fn set_model(&mut self, model: String) {
        self.model = model;
    }
}
